LLM Temperature and Safety Tradeoffs

Temperature controls the randomness of language model outputs. A temperature of 0 produces deterministic, highest-probability completions. Higher values increase randomness. This parameter has direct implications for safety that many teams overlook.

How Temperature Affects Safety

Safety-trained models learn to assign high probability to safe refusals when presented with harmful prompts. At low temperature, the model reliably selects these high-probability safe responses. As temperature increases, lower-probability tokens become viable candidates, and unsafe completions that the model would normally avoid can surface.

Research shows that jailbreak success rates increase with temperature. At temperature 0, a well-trained model might refuse a harmful request 99.9% of the time. At temperature 1.5, that refusal rate can drop measurably because the sampling process reaches into the distribution tail where unsafe completions live.

Practical Guidelines

For safety-critical agent actions like executing code, making API calls, or modifying data, use low temperature values between 0 and 0.3. The tradeoff in creativity is worth the safety improvement.

For creative tasks where higher temperature is desirable, apply stricter output filtering. The increased randomness that makes creative writing better also makes safety violations more likely.

Never expose temperature as a user-configurable parameter in production agents. An attacker who can increase temperature has a straightforward path to bypassing safety training.

Temperature in Multi-Step Agents

Agents that use chain-of-thought reasoning or multi-step planning accumulate risk across steps. Each step at elevated temperature has an independent chance of producing unsafe output. Over a ten-step reasoning chain, even modest per-step risk compounds.

Consider using different temperature settings for different stages. Planning and tool selection steps benefit from low temperature. Summary and explanation steps can tolerate higher values.

Authensor's policy engine evaluates each action independently, so even if a high-temperature step produces an unsafe tool call, the policy layer catches it before execution. Runtime guardrails compensate for the inherent unpredictability of temperature-based sampling.